Fortifying Healthcare and Essential Infrastructure with AI-Driven Cybersecurity Technologies

Abstract

Digitization introduces new complexities in cyberattacks for healthcare and critical infrastructure sectors due to risks associated with sensitive data such as Protected Health Information. This article examines AI-enabled cybersecurity in healthcare for anomaly detection, prevention of intrusions, and threat intelligence employing machine learning and deep learning for autonomous real-time defense. AI-based intrusion prevention systems targeting endpoint protection and mitigation of behavior-based threats are reviewed alongside overcoming model accuracy, tolerance, and privacy issues. It is posited an inclusive framework that employs AI and blockchain for secure verifiability of data emphasizes the credibility of trusted data, however, still requires substantiation. We focus on comparative analysis of AI models using real-world datasets MHealth, CICIDS 2017, and NSL-KDD, achieving higher detection rates coupled with fewer false positives and reduced processing times. Increased dataset and real-world simulated cyberattack validations are suggested as a next step to validate anticipatory effectiveness claims.

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